ICCV 20223

Former-commit-id: 1a73f9bf22ef47e51ade315e044a587fe66d701f

README.md

@@ -93,13 +93,17 @@ The performance is reported on Urban100 (x4). The test input size of FLOPs is 12
 
 ## Testing
 
+### Test images with HR
+
 - Download the pre-trained [models](https://drive.google.com/drive/folders/1iBdf_-LVZuz_PAbFtuxSKd_11RL1YKxM?usp=drive_link) and place them in `experiments/pretrained_models/`.
 
   We provide pre-trained models for image SR: DAT-S, DAT, and DAT-2 (x2, x3, x4).
 
 - Download [testing](https://drive.google.com/file/d/1yMbItvFKVaCT93yPWmlP3883XtJ-wSee/view?usp=sharing) (Set5, Set14, BSD100, Urban100, Manga109) datasets, place them in `datasets/`.
 
-- Run the following scripts. The testing configuration is in `options/test/`.
+- Run the following scripts. The testing configuration is in `options/test/` (e.g., [test_DAT_x2.yml](options/Test/test_DAT_x2.yml)).
+
+  Note 1:  You can set `use_chop: True` (default: False) in YML to chop the image for testing.
 
   ```shell
   # No self-ensemble
@@ -121,6 +125,28 @@ The performance is reported on Urban100 (x4). The test input size of FLOPs is 12
 
 - The output is in `results/`.
 
+### Test images without HR
+
+- Download the pre-trained [models](https://drive.google.com/drive/folders/1iBdf_-LVZuz_PAbFtuxSKd_11RL1YKxM?usp=drive_link) and place them in `experiments/pretrained_models/`.
+
+  We provide pre-trained models for image SR: DAT-S, DAT, and DAT-2 (x2, x3, x4).
+
+- Put your dataset (single LR images) in `datasets/single`. Some test images are in this folder.
+
+- Run the following scripts. The testing configuration is in `options/test/` (e.g., [test_single_x2.yml](options/Test/test_single_x2.yml)).
+
+  Note 1: The default model is DAT. You can use other models like DAT-S by modifying the YML.
+  Note 2:  You can set `use_chop: True` (default: False) in YML to chop the image for testing.
+
+  ```shell
+  # Test on your dataset
+  python basicsr/test.py -opt options/Test/test_single_x2.yml
+  python basicsr/test.py -opt options/Test/test_single_x3.yml
+  python basicsr/test.py -opt options/Test/test_single_x4.yml
+  ```
+
+- The output is in `results/`.
+
 ## Results
 
 We achieved state-of-the-art performance. Detailed results can be found in the paper. All visual results of DAT can be downloaded [here](https://drive.google.com/drive/folders/1ZMaZyCer44ZX6tdcDmjIrc_hSsKoMKg2?usp=drive_link).